Improving Pavement Crack Segmentation Using Attention Mechanism and Self-gated Activation
Bibliographic record
Abstract
Image segmentation is crucial in various applications, from autonomous driving, agriculture, and manufacturing to medical imaging and satellite imaging. It helps a computer vision-based system perceive the overall scene by identifying and delineating different objects or regions. However, pavement crack segmentation remains one of the most intriguing tasks due to several factors, like irregular shapes and sizes, uneven illumination, complex backgrounds, and noise. This study proposes a lightweight pavement crack segmentation model that harnesses attention mechanisms and self-gated activation to improve the results. Exhaustive experimental analyses on two benchmark datasets demonstrate that the model outperforms the existing solutions by achieving an overall mean intersection over union (mIoU) of 69.3%, which is an improvement of 1.85% compared to the baseline. Hence, the model’s per-sample inference time is 239±7 ms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".